The invention relates to a method for discriminating between a real face a two-dimensional image of the face in a biometric detection process and to a method of identifying a person which uses this discrimination method.
Biometric identification of persons based on faced recognition is known. Digital images of a face, for example, can be compared to a reference photography of the face which, by way of example, can have been made according to the Guidelines of the ICAO (International Civil Aerospace Organization) according to the biometrical standard ICAO 9303 (Photograph Guideline). Matching between the photograph and a digital recording is routinely done at many border control stations. It is also possible to match the digital recording of a person with a recording of the same person stored in a data base so as to grant the person access to equipment, computers, applications in the internet, and the like. A method for the biometric recognition of persons is described, e.g. in German patent DE 198 47 261, corresponding to U.S. Pat. No. 6,999,606.
Biometric methods for recognizing persons are deemed to be particularly reliable because they use features specific to the person. However, biometric recognition of persons is not completely forgery-proof. In face recognition, for example, there is the risk that a person does not identify himself or herself using his or her own real face but rather using the photograph of another person if not supervising control personnel is present during the inspection. For example, in a distributed IT infrastructure, such as a cloud computing environment, or simply for using an application in the internet, a person might want to identify himself or herself by face recognition for gaining access to a particular system or application. As a rule, the person can do so at any arbitrary location, including his or her own home. For such cases, it must be excluded that the person identifies himself or herself using a photograph instead of the real face.
It is possible to discriminate between a real face and its image by evaluating geometrical features for distinguishing between a two-dimensional plane of the photograph and a three-dimensional structure of the face. In the prior art, it is known to derive such geometric aspects from at least two recordings using stereoscopy wherein a three-dimensional structure is derived using calibrated cameras. The principle of stereoscopy is based on the fact that, similar to the human eyes, an object is observed and recorded from two different viewing angles at the same time. The position of predetermined typical features of the face, such as the corner of the mouth, the tip of the nose and the like, then are determined in both images and the differences of the positions are used for determining depth information. This allows to distinguish between a three-dimensional face and a two-dimensional photograph. However, this process requires the use of specialized cameras and hence, for many applications and circumstances, is not feasible.
In the area of face recognition, it is also known, for example, to make a number of recordings from a person and to determine whether there are intrinsic movements within the face, by comparing the recordings, so as to exclude the use of a photograph. US 20009/0135188 A1 describes a system for biometric identification and verification of a person and for discriminating between a real human face and a photograph of the face by online detection of physiological movements of the face. For determining face movement, characteristic areas, such as the eyes and the mouth, are localized and the distance between the center of the face and the coordinates of e.g. the eyes and the mouth are calculated. If no movement of the face is detected, it is assumed that a photograph is present. In addition, it is determined whether the surrounding outside of the face region is moving and, if yes, it is assumed that a photograph of the face is present. The method requires localizing characteristic areas of the face, such as eyes and mouth, and overall, does not appear to be very reliable.
EP 1 434 163 B1 describes a method for detecting and verifying a face using biometric processes which is also based on the localization of characteristic features of the face. On the basis of a number of detected data, the perceived shapes of different face orientations are calculated so as to create a dictionary for each person which, for example, includes differences in the distance between the eyes or between the eyes and the nose as a function of different face orientations. The method appears to be rather demanding in computing and storage resources and might be suitable for closed systems rather than distributed IT structures, such as a cloud computing environment, which limit the users in terms of amount of data which can be transmitted and available computing power.
For such an environment it would be ideal to be able to realize the recognition of persons on the basis of a small amount of data, including only two to five or a maximum of ten images, for example. The identification and verification should be possible with little computing complexity, and might be based on only one reference image so as to efficiently use storage and calculating resources.
Chia-Ming Wang et al., in “Distinguishing Falsification of Human Faces from True Faces based on Optical Flow Information”, IEEE International Symposium on Circuits and Systems, 2009, describes a system for discriminating between a real face and a two-dimensional image, i.e. a photograph, of the face based on models of movement. Using an optical flow method, on the basis of at least five subsequent images, a movement model is created wherein the movement model will be different for real faces and photographic replications, wherein these differences can be evaluated for discriminating between real faces and photographies. A LDA-based (LDA=Linear Discriminant Analysis) learning method and a Bayes classifier are used for discriminating the movement fields of real faces and those of photographies. The method provides good results, however, it requires substantial computing and storage resources as well as high data transmission power if it should be used in a distributed IT environment. It further requires a substantial training process on the basis of different test faces before it is ready to be used.
Tanzeem Choudhury et al., in “Multimodel Person Recognition using Unconstrained Audio and Video”, MIT Media Lab AV BPA, 1999, describe that, in general, it is possible to perform a movement analysis for discriminating between real faces and photographies and to estimate the depth of each feature therefrom. It is assumed that objects, the features of which all have the same depth, are photographies, whereas other objects are real faces. The document does not describe any details how this method is to be performed.
Chao-Kuei Hsieh et al., in “An Optical Flow-Based Approach to Robust Face Recognition Under Expression Variations”, IEEE Transactions on Image Processing, 2010, describe a face recognition method using optical flow methods wherein the optical flow within a face is calculated for compensating differences in different facial expressions.
Bruce D. Lucas et al., in “An Iterative Image Registration Technique with an Application to Stereo Vision”, Proceedings of Image Understanding Workshop, 1981, describe a method for localizing a template G(x) within an image F(x) using the L1 standard and the L2 standard and explain different techniques of correlation, including the sequential similarity detection algorithm (SSDA).
Optical correlation methods which can be used in the present invention, are also described in the doctorate thesis of the inventor: R. Frischholz, “Beiträge zur Automatischen Dreidimensionalen Bewegungs analyse” (ISBN3-8265-3733-5), Dissertation, Shaker Verlag, 1998. Reference is made to these documents, which are incorporated herein by reference, in particular to the explanation of optical flow methods and correlation methods.
In the prior art, it hence is known to use optical flow methods to discriminate between real faces and photographic reproductions during biometric face recognition. In image processing and in optical measurement technology, an optical flow designates a vector field which indicates the direction and speed of movement of each pixel (image point) of an image sequence. The optical flow can be a starting point for detecting three-dimensional structures for estimating movement in space and for recognizing individual moving objects. Classical optical flow methods are differential methods, i.e. they are based on the derivation and gradients of a gray level signal which are derived on a pixel basis. The calculation of the optical flow, using differential methods, can be traced back to a method of Berthold Horn and Brian Schunk which was developed at the MIT (Massachusetts Institute of Technology) in 1981 (Berthold K. P. Horn et al., “Determining Optical Flow”, Artificial Intelligence, Volume 17, No. 1-3, pages 185-203, 1981; which is incorporated herein by reference).
While, in theory, optical flow methods are suitable for discriminating between reproductions of real persons and photographic images, in practice, there are a number of problems: The pixel-based calculation of optical flow requires high computing resources which aggravates inspection within a reasonable time frame. Due to the high noise factor of the pixels of the digital recording, it is necessary to even out the flow vectors generated over a number of images which, again, increases the amount of data and computing requirements. Nevertheless, optical flow methods remain error prone.